Search Results (385)

Ceratitis capitata (Wiedemann) or medfly is a polyphagous pest of fruit crops worldwide. The Asian-native larval parasitoid Diachasmimorpha longicaudata (Ashmead) is mass-reared at the San Juan Biofactory and is currently released for medfly control in Argentina. Information on parasitoid survival, reproduction, and population growth parameters is critical for optimizing the mass-rearing process and successfully achieving large-scale release. This study provides a first-time insight into the demography of two population lines of D. longicaudata: one mass-reared on medfly larvae of the Vienna-8 temperature-sensitive lethal genetic sexing strain and the other on larvae of the wild biparental medfly strain. The aim was to compare both parasitoid populations to improve mass-rearing quality and to assess performance on medfly in a semi-arid environment, typical of Argentina’s central-western fruit-growing region. Tests were performed under laboratory and non-controlled environmental conditions in semi-field cages during three seasons. Dl_(Cc-bip) females exhibited higher reproductive potential than did Dl_(Cc-tsl) females under lab conditions. However, both Dl_(Cc-bip) and Dl_(Cc-tsl) were found to be similar high-quality females with high population growth rates in warm–temperate seasons, i.e., late spring and summer. Dl_(Cc-bip) females were only able to sustain low reproductive rates in early autumn, a colder season. These results are useful for improving the parasitoid mass production at the San Juan Biofactory and redesigning parasitoid release schedules in Argentina’s irrigated, semi-arid, fruit-growing regions. Full article

Effective production control requires aligning strategic planning with real-time execution under dynamic and stochastic conditions. This study proposes a scalable dual-agent Deep Reinforcement Learning (DRL) framework for the joint optimisation of Work-In-Process (WIP) control and job sequencing in flow-shop environments. A strategic DQN agent regulates global WIP to meet throughput targets, while a tactical DQN agent adaptively selects dispatching rules at the machine level on an event-driven basis. Parameter sharing in the tactical agent ensures inherent scalability, overcoming the combinatorial complexity of multi-machine scheduling. The agents coordinate indirectly via a shared simulation environment, learning to balance global stability with local responsiveness. The framework is validated through a discrete-event simulation integrating agent-based modelling, demonstrating consistent performance across multiple production scales (5–15 machines) and process time variabilities. Results show that the approach matches or surpasses analytical benchmarks and outperforms static rule-based strategies, highlighting its robustness, adaptability, and potential as a foundation for future Hierarchical Reinforcement Learning applications in manufacturing. Full article

Accurate docking of Autonomous Guided Vehicles (AGVs) is a critical requirement for efficient automated production systems in Industry 4.0, particularly for collaborative tasks with robotic arms that have a limited working range. This paper introduces a cost-effective software-upgrade solution to enhance the precision of the final docking phase without requiring new hardware. Our approach is based on a two-stage strategy: first, a switch from a global dead reckoning system to a local navigation scheme, is triggered near the docking station; second, a dedicated Takagi-Sugeno Fuzzy Logic Controller (FLC), guides the AGV to its final position with high accuracy. The core novelty of our FLC is its implementation as a gain-scheduling lookup table (LUT), which synthesizes critical state variables—heading error and distance error—from limited proximity sensor data, to robustly handle positional uncertainty and environmental variations. This method directly addresses the inadequacies of traditional odometry, whose cumulative errors become unacceptable at the critical docking point. For experimental validation, we assume the global navigation delivers the AGV to a general switching point, near the assembly station with an unknown true pose. We detail the design of the fuzzy controller and present experimental results that demonstrate a significant improvement, achieving repeatable docking accuracy within industrially acceptable tolerances. Full article

Ensuring optimal adhesive curing during plywood and LVL (Layered Veneer Lumber) hot pressing requires accurate knowledge of internal temperature distribution, which is often difficult to assess using conventional surface-based measurements. This study introduces a custom-developed multi-layer smart sensor network capable of in situ, real-time temperature profiling across LVL layers during industrial hot pressing. The system integrates miniature embedded sensors and proprietary data acquisition software, enabling the simultaneous multi-point monitoring of thermal dynamics with a high temporal resolution. Experiments were performed on LVL panels of varying thicknesses, applying industry-standard pressing schedules derived from conventional calculation rules. Despite adherence to prescribed pressing times, results reveal significant core temperature deficits in thicker panels, potentially compromising adhesive gelation and overall bonding quality. These findings underline the need to revisit the pressing time determination for thicker products and demonstrate the potential of advanced sensing technologies to support adaptive process control. The proposed approach contributes to smart manufacturing and the remote-like monitoring of internal thermal states, providing valuable insights for enhancing product performance and industrial process efficiency. Full article

The resilience and sustainable development of modern power distribution systems faces escalating challenges due to increasing renewable integration and extreme events. Traditional single-system approaches often overlook the spatiotemporal coordination of cross-domain restoration resources. In this paper, we propose a multi-stage resilience enhancement method that employs transportation and hydrogen energy systems. This approach coordinates the pre-event preventive allocation and multi-stage collaborative scheduling of diverse restoration resources, including remote-controlled switches (RCSs), mobile hydrogen emergency resources (MHERs), and hydrogen production and refueling stations (HPRSs). The proposed framework supports cross-stage dynamic optimization scheduling, enabling the development of adaptive resource dispatch strategies tailored to the characteristics of different stages, including prevention, fault isolation, and service restoration. The model is applicable to complex scenarios involving dynamically changing network topologies and is formulated as a mixed-integer linear programming (MILP) problem. Case studies based on the IEEE 33-bus system show that the proposed method can restore a distribution system’s resilience to approximately 87% of its normal level following extreme events. Full article

The search for higher efficiency in agribusiness supports the adoption of digital tools and Lean Production principles in agricultural spraying, a crucial operation for crops. Spraying is essential to ensure yield, quality, cost efficiency, and environmental protection. This study analyzed operational data from self-propelled sprayers in soybean and corn fields, classifying hours, calculating efficiencies, and applying statistical process control. Efficiencies were investigated by combining Lean Production principles with CAN-based digital monitoring, which enabled the identification of non-value-adding activities and supported the real-time management of spraying operations. The results showed that productive time accounted for 41.2% of total recorded hours, corresponding to effective operation and auxiliary tasks directly associated with the execution of spraying activities. A high proportion of unrecorded hours (21.2%) was also observed, reflecting discrepancies between administrative work schedules and machine-logged data. Additionally, coefficients of variation for operational speed and fuel consumption were 12.1% and 24.0%, respectively. Correcting special causes increased work capacity (4.9%) and reduced fuel consumption (0.9%). Economic simulations, based on efficiencies, operating parameters of the sprayer, and cost indicators, indicated that increasing scale reduces costs when installed capacity is carefully managed. Integrating telemetry with Lean Production principles enables real-time resource optimization and waste reduction. Full article

Production flow control is a key area affecting the productivity of production systems. The use of an appropriate control method ensures that customer requirements are met while maintaining an acceptable level of production costs. In many cases, the choice of control method does not allow for significant improvements in production processes, as the known guidelines are not very detailed. This article presents research on the impact of factors related to products, production processes, and customer orders on, for example, the number of technological operations, the number of production stations, product demand (product, process, and order conditions—PPOC), and the effectiveness of production flow control methods. This research was conducted for selected product families (water and gas fittings) for which various production flow control solutions were developed. The most popular control methods were used: push–schedule, supermarket-type pull, sequential pull, mixed pull, and drum-buffer-rope. The criteria for evaluation were in-process stocks and lead time of materials in the production process. As a result of the ranking, relationships were identified by indicating how the values of PPOC factors affect the effectiveness of a given production flow control method. The results of this research can serve as guidelines for companies in selecting the most appropriate method of controlling production processes. Full article

Background/Objectives: The approach to physical activity in people with hemophilia (PwH) is still conditioned by many difficulties. Thus, a prospective observational pilot study has been carried out aiming to evaluate how an adequate and controlled training program can slow down the onset or evolution of arthropathy and improve musculoskeletal health and quality of life. Methods: Performed from April 2022 to April 2023, this study involved nine severe hemophilic A and B patients, aged > 18 years old, on regular prophylaxis with replacement products. Participants, without changing the usual prophylaxis schedule and maintaining a trough level of at least 20% FVIII/FIX before training, were involved in physical activity twice a week. Results: After 12 months, no increase in annual bleeding ratio (ABR) was observed, and baseline joint status (as assessable by HEAD US score, HJHS, and NRS) was maintained. Even if not statistically significant, a trend toward improvement in mean HEAD US score (15.55 vs. 13.11) and HJHS (14.4 vs. 11) from baseline was observed. Some of the physical tests performed showed a significant improvement at 6 months and 12 months from baseline (5 Rep Sit to Stand, Sit and Reach, and 6-minute Walking Test), meaning an improvement in leg strength, dorsal flexibility, and aerobic resistance. Conclusions: This is the first pilot study evaluating at 360 degrees the safety and impact of a controlled physical activity in PwH. No participant experienced bleedings or a worsening in joint status, but they experienced an improvement in articular functionality. Without changing the usual prophylaxis, scheduling training sessions according to individual pharmacokinetics turned out to be a safe and a cost-effective approach. Full article

According to United Nations projections, the global population is expected to reach 9.7 billion by 2050, with 70% residing in urban areas, while arable land availability continues to decline. Vertical farming (VF) offers a promising pathway for sustainable urban food production by utilizing vertical space and controlled environments. Among emerging approaches, the adaptive vertical farm (AVF) introduces movable shelving systems that adjust to plant growth stages, allowing a higher number of cultivation shelves to be accommodated within the same rack height. In this study, we developed a computational model to quantify and compare the energy consumption of AVF and conventional VF systems under industrial-scale conditions. The reference scenario considered 272 multilevel racks, each hosting 8 shelves in the VF and 15 shelves in the AVF, with Lactuca sativa as the test crop. Energy consumption for thermohygrometric control and lighting was estimated under different sowing schedules, with crop growth dynamics simulated using scheduling algorithms. Plant heat loads were calculated through the Penman–Monteith model, enabling a robust estimation of evapotranspiration and its impact on indoor climate control. Simulation results show that the AVF achieves an average 22% reduction in specific energy consumption for climate control compared to the VF, independently of sowing strategies. Moreover, the AVF nearly doubles the number of cultivation shelves within the same footprint, increasing the cultivable surface area by over 400% compared to traditional flat indoor systems. This work provides the first quantitative assessment of AVF energy performance, demonstrating its potential to simultaneously improve land-use efficiency and reduce energy intensity, thereby supporting the sustainable integration of vertical farming in urban food systems. Full article

For the first time, this study presents a cyber–physical framework that reconciles the long-standing conflict between transparent queue analytics and adaptive machine learning in flexible manufacturing systems. Deterministic indicators, utilization, expected queue length, waiting time, and idle probability, are fused with topological embeddings of the routing graph and ingested by a graph convolutional network that predicts station congestion with calibrated confidence intervals. Shapley additive explanations decompose every forecast into causal contributions, and these vectors, together with a percentile-based risk metric, steer a mixed-integer genetic optimizer toward schedules that lift throughput without breaching statistical congestion limits. A cloud dashboard streams forecasts, risk bands, and color-coded explanations, allowing supervisors to accept or modify suggestions; each manual correction is logged and injected into nightly retraining, closing a socio-technical feedback loop. Experiments on an 8704-cycle production census demonstrate a 38 percent reduction in average queue length and a 12 percent rise in throughput while preserving full audit traceability, enabling one-minute rescheduling on volatile shop floors. The results confirm that transparency and adaptivity can coexist when analytical priors, explainable learning, and risk-aware search are unified in a single containerized control stack. Full article

Efficient labor productivity forecasting is a critical challenge in construction engineering, directly influencing scheduling, cost control, and resource allocation. In reinforced concrete projects, accurate prediction of rebar-fixing productivity enables managers to optimize workforce deployment and mitigate delays. This study proposes a machine learning-based framework to forecast rebar-fixing labor productivity under varying site and environmental conditions. Four regression algorithms—Random Forest (RF), Extreme Gradient Boosting (XGBoost), Support Vector Regression (SVR), and k-Nearest Neighbors (KNN)—were trained, tuned, and validated using grid search with k-fold cross-validation. RF achieved the highest accuracy, with an $R^2$ of 0.901 and RMSE of 19.94 on the training set and an $R^2$ of 0.877 and RMSE of 22.47 on the test set, indicating strong generalization. Model interpretability was provided through SHapley Additive exPlanations (SHAP), revealing that larger quantities of M32 and M25 rebars increased productivity, while higher temperatures reduced it, likely due to lower labor efficiency. Humidity, wind speed, and precipitation showed minimal influence. The integration of accurate predictive modeling with explainable machine learning offers practical insights for project managers, supporting data-driven decisions to enhance reinforcement task efficiency in diverse construction environments. Full article

Crop aeroponic cultivation still faces issues such as insufficient precision in water supply control and scientifically-based irrigation scheduling. To address this challenge, the present study aims to establish a precision irrigation protocol adapted to the characteristics of crop aeroponic cultivation. Using coriander (Coriandrum sativum L.) as the experimental subject, crop water requirements were estimated utilizing both the FAO56 P-M equation and its revised form. The RMSE between the water requirement measured values and the calculated values using the P-M formula is 2.12 mm, the MAE is 2.0 mm, and the MAPE is 14.29%. The RMSE between the water requirement measured values and the calculated values using the revised P-M formula is 0.88 mm, the MAE is 0.82 mm, and the MAPE is 5.78%. The results indicate that the water requirement values calculated using the revised P-M formula are closer to the measured values. For model development, this study used coriander evapotranspiration as a basis. Major environmental variables influencing water requirement were selected as input features, and the daily reference water requirement served as the output. Three modeling approaches were implemented: Random Forest (RF), Bagging, and M5P Model Tree algorithms. The results indicate that, in comparing various input combinations (C1: air temperature, relative humidity, atmospheric pressure, wind speed, radiation, photoperiod; C2: air temperature, relative humidity, wind speed, radiation; C3: air temperature, relative humidity, radiation), the RF model based on C1 input demonstrated superior performance with RMSE = 0.121 mm/d, MAE = 0.134 mm/d, MAPE = 2.123%, and R² = 0.971. It significantly outperforms the RF models with other input combinations, as well as the Bagging and M5P models across all input scenarios, in terms of convergence rate, determination coefficient, and comprehensive performance. Its predictions aligned more closely with observed data, showing enhanced accuracy and adaptability. This optimized prediction model demonstrates particular suitability for forecasting water requirements in aeroponic coriander production and provides theoretical support for efficient, intelligent water-saving management in crop aeroponic cultivation. Full article

Power batteries are one of the important components of electric vehicles, but the manufacturing process of vehicle power batteries is complex and diverse. Traditional scheduling methods face challenges such as low production efficiency and inadequate quality control in complex production environments. To address these issues, a multi-objective optimization model with makespan, total machine load, and processing quality as the established objectives, and a Multi-objective Particle Swarm Energy Valley Optimization (MPSEVO) is proposed to solve the problem. MPSEVO integrates the advantages of Multi-objective Particle Swarm Optimization (MOPSO) and Energy Valley Optimization (EVO). In this algorithm, the particle stability level is combined in MOPSO, and different update strategies are used for particles of different stability to enhance both the convergence and diversity of the solutions. Furthermore, a local search strategy is designed to further enhance the algorithm to avoid the local optimal solutions. The Hypervolume ($HV$) and Inverted Generational Distance ($IGD$) indicators are often used to evaluate the convergence and diversity of multi-objective algorithms. The experimental results show that MPSEVO’s $HV$ and $IGD$ indicators are better than other algorithms in 10 computational experiments, which verifies the effectiveness of the proposed strategy and algorithm. The proposed method is also applied to solve the actual battery workshop scheduling problem. Compared with MOPSO, MPSEVO reduces the total machine load by 7 units and the defect rate by 0.05%. In addition, the effectiveness of each part of the improved algorithm was analyzed by ablation experiments. This paper provides some ideas for improving the solution performance of MOPSO, and also provides a theoretical reference for enhancing the production efficiency of the vehicle power battery manufacturing workshop. Full article

To maximize the benefits of power generation and water supply of the reservoir under the premise of ensuring ecological flow as much as possible, it is necessary to formulate a highly operational release scheme in the actual production scheduling process. To mitigate the ecological impacts of reservoir operations, enhanced environmental flow releases are required; however, this results in diminished reservoir economic outputs. Therefore, in order to determine the government subsidy standards for ecological regulation of reservoirs and improve the enthusiasm of water conservancy departments for ecological regulation, it is necessary to conduct comprehensive analysis and research on the benefits of ecological regulation. According to the ecological releases of the reservoir, the reservoir operation scheme is formulated, and the comprehensive benefits of the reservoir operation are analyzed and studied to determine the optimal operation scheme. Based on the monthly inflow runoff of the Baishi Reservoir to the Daling River from 1956 to 2011, constrained by the ecological base flow specified by the government, and combined with the water supply and power generation functions of the reservoir, an optimal operation model of the Baishi Reservoir based on ecological release is constructed. The water supply, power generation, and ecological benefits of the reservoir discharge are comprehensively analyzed and calculated to analyze and study the loss of economic benefits caused by the reservoir discharge and the ecological benefits that can be obtained from the ecological discharge. Based on the comprehensive evaluation of multiple indicators, the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) fuzzy comprehensive evaluation method is used to select the optimal scheduling scheme. The optimal scheduling plan for a reservoir is closely related to its characteristic water level. In order to improve the efficiency of reservoir scheduling, monthly control of reservoir discharge can be implemented. The guarantee rate of urban domestic water supply and ecological water use can be increased as much as possible, while the guarantee rate of agricultural water use can be appropriately reduced to obtain the optimal comprehensive benefits. The outflow considering ecological release is 6.5–7 m³/s from June to April and 1 m³/s in May. The outflow without considering ecological release is 4 m³/s from June to April and 1 m³/s in May. This study has certain guiding significance and value for the formulation of an ecological operation scheme for reservoirs and the analysis of benefits. Full article

This paper addresses the challenge of renewable energy curtailment, which stems from the inherent uncertainty and volatility of wind and photovoltaic (PV) generation, by developing a robust model predictive control (RMPC)-based scheduling strategy for an integrated wind–PV–hydrogen storage multi-energy flow system. By building a “wind–PV–hydrogen storage–fuel cell” collaborative system, the time and space complementarity of wind and PV is used to stabilize fluctuations, and the electrolyzer–hydrogen production–gas storage tank–fuel cell chain is used to absorb surplus power. A multi-time scale state-space model (SSM) including power balance equation, equipment constraints, and opportunity constraints is established. The RMPC scheduling framework is designed, taking the wind–PV joint probability scene generated by Copula and improved K-means and SSM state variables as inputs, and the improved genetic algorithm is used to solve the min–max robust optimization problem to achieve closed-loop control. Validation using real-world data from Xinjiang demonstrates a 57.83% reduction in grid power fluctuations under extreme conditions and a 58.41% decrease in renewable curtailment rates, markedly enhancing the local system’s capacity to utilize wind and solar energy. Full article